JTh2SSKrSSKJrJrJrJr SSKJrJrJ r J r SSK r /SQr \ "SSS9r "SS \\ 5r"S S \\5r"S S \\5r"SS\\5r"SS\\5r"SS\\\5rg)N)IterableIteratorSequenceSized)GenericOptionalTypeVarUnion) BatchSampler RandomSamplerSamplerSequentialSamplerSubsetRandomSamplerWeightedRandomSampler_T_coT) covariantcH\rSrSrSrS S\\SS4SjjrS\\ 4Sjr Sr g) r aBase class for all Samplers. Every Sampler subclass has to provide an :meth:`__iter__` method, providing a way to iterate over indices or lists of indices (batches) of dataset elements, and may provide a :meth:`__len__` method that returns the length of the returned iterators. Args: data_source (Dataset): This argument is not used and will be removed in 2.2.0. You may still have custom implementation that utilizes it. Example: >>> # xdoctest: +SKIP >>> class AccedingSequenceLengthSampler(Sampler[int]): >>> def __init__(self, data: List[str]) -> None: >>> self.data = data >>> >>> def __len__(self) -> int: >>> return len(self.data) >>> >>> def __iter__(self) -> Iterator[int]: >>> sizes = torch.tensor([len(x) for x in self.data]) >>> yield from torch.argsort(sizes).tolist() >>> >>> class AccedingSequenceLengthBatchSampler(Sampler[List[int]]): >>> def __init__(self, data: List[str], batch_size: int) -> None: >>> self.data = data >>> self.batch_size = batch_size >>> >>> def __len__(self) -> int: >>> return (len(self.data) + self.batch_size - 1) // self.batch_size >>> >>> def __iter__(self) -> Iterator[List[int]]: >>> sizes = torch.tensor([len(x) for x in self.data]) >>> for batch in torch.chunk(torch.argsort(sizes), len(self)): >>> yield batch.tolist() .. note:: The :meth:`__len__` method isn't strictly required by :class:`~torch.utils.data.DataLoader`, but is expected in any calculation involving the length of a :class:`~torch.utils.data.DataLoader`. N data_sourcereturnc6UbSSKnURS5 gg)Nrzz`data_source` argument is not used and will be removed in 2.2.0.You may still have custom implementation that utilizes it.)warningswarn)selfrrs P/var/www/auris/envauris/lib/python3.13/site-packages/torch/utils/data/sampler.py__init__Sampler.__init__@s"  "  MMM  #c[eN)NotImplementedErrorrs r__iter__Sampler.__iter__Is!!rr ) __name__ __module__ __qualname____firstlineno____doc__rrrrrr#__static_attributes__r%rrr r s/'RHUOt"(5/"rr cX\rSrSr%Sr\\S'S\SS4SjrS\\ 4Sjr S\ 4Sjr S r g) rhzrSamples elements sequentially, always in the same order. Args: data_source (Dataset): dataset to sample from rrNcXlgr r)rrs rrSequentialSampler.__init__qs&rcP[[[UR555$r )iterrangelenrr"s rr#SequentialSampler.__iter__tsE#d../011rc,[UR5$r )r4rr"s r__len__SequentialSampler.__len__ws4##$$rr/) r&r'r(r)r*r__annotations__rrintr#r7r+r%rrrrhs> 'E'd'2(3-2%%rrc \rSrSr%Sr\\S'\\S'S S\S\S\\ SS4Sjjr \ S\ 4S j5r S\ \ 4S jrS\ 4S jrS rg)r {aSamples elements randomly. If without replacement, then sample from a shuffled dataset. If with replacement, then user can specify :attr:`num_samples` to draw. Args: data_source (Dataset): dataset to sample from replacement (bool): samples are drawn on-demand with replacement if ``True``, default=``False`` num_samples (int): number of samples to draw, default=`len(dataset)`. generator (Generator): Generator used in sampling. r replacementN num_samplesrc0XlX lX0lX@l[ UR[ 5(d[ SUR35e[ UR[5(aURS::a[SUR35eg)N;replacement should be a boolean value, but got replacement=rDnum_samples should be a positive integer value, but got num_samples=) rr= _num_samples generator isinstancebool TypeErrorr>r: ValueError)rrr=r>rCs rrRandomSampler.__init__s'&'"$**D11MdN^N^M_` $**C00D4D4D4IVW[WgWgVhi 5Jrc^URc[UR5$UR$r )rBr4rr"s rr>RandomSampler.num_sampless-    $t''( (   rc## [UR5nURco[[R "S[R S9R5R55n[R"5nURU5 O URnUR(a[URS-5H>n[R"US[R US9R5ShvN M@ [R"UURS-4[R US9R5ShvN g[URU-5H-n[R "XS9R5ShvN M/ [R "XS9R5SURU-ShvN gNNNGN 7f)Nr%dtype )rN)highsizerMrCrC)r4rrCr:torchemptyint64random_item Generator manual_seedr=r3r>randinttolistrandperm)rnseedrC_s rr#RandomSampler.__iter__sy   ! >> !u{{2U[[9AACHHJKD)I  ! !$ 'I   4++r12 ==ekkY&(3}}&&+-kk#  fh   4++q01 >>!AHHJJJ2~~a=DDF&$""Q&   K sJDG!GA G!GAG!G=G!GG!G!G!G!cUR$r r>r"s rr7RandomSampler.__len__r)rBrrCr=)FNN)r&r'r(r)r*rr9rErr:rpropertyr>rr#r7r+r%rrr r {s  "%) c]   ,!S!! (3-6  rr ch\rSrSr%Sr\\\S'S S\\SS4SjjrS\ \4Sjr S\4Sjr S r g) rzSamples elements randomly from a given list of indices, without replacement. Args: indices (sequence): a sequence of indices generator (Generator): Generator used in sampling. indicesNrcXlX lgr )rgrC)rrgrCs rrSubsetRandomSampler.__init__s  "rc## [R"[UR5URS9HnURUv M g7fNrQ)rRr[r4rgrC)ris rr#SubsetRandomSampler.__iter__s6DLL 1T^^LA,,q/ !MsA Ac,[UR5$r )r4rgr"s rr7SubsetRandomSampler.__len__s4<<  r)rCrgr ) r&r'r(r)r*rr:r9rrr#r7r+r%rrrrsFc]# #$#"(3-"!!rrc \rSrSr%Sr\R \S'\\S'\ \S'S S\ \ S\S\ SS4Sjjr S\ \4S jrS\4S jrS rg) raSamples elements from ``[0,..,len(weights)-1]`` with given probabilities (weights). Args: weights (sequence) : a sequence of weights, not necessary summing up to one num_samples (int): number of samples to draw replacement (bool): if ``True``, samples are drawn with replacement. If not, they are drawn without replacement, which means that when a sample index is drawn for a row, it cannot be drawn again for that row. generator (Generator): Generator used in sampling. Example: >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> list(WeightedRandomSampler([0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True)) [4, 4, 1, 4, 5] >>> list(WeightedRandomSampler([0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False)) [0, 1, 4, 3, 2] weightsr>r=Nrc[U[5(a[U[5(dUS::a[SU35e[U[5(d[SU35e[R "U[R S9n[UR5S:wa![S[UR535eXPl X l X0l X@l g)NrrAr@rLz=weights should be a 1d sequence but given weights have shape )rDr:rErGrR as_tensordoubler4shapetuplerrr>r=rC)rrrr>r=rCweights_tensors rrWeightedRandomSampler.__init__s;,,+t,,aVWbVcd +t,,Mk][  E ~## $ )&&+N,@,@&A%BD  & &&"rc## [R"URURURUR S9n[ UR55ShvN gN7frk)rR multinomialrrr>r=rCr2rZ)r rand_tensors rr#WeightedRandomSampler.__iter__sK'' LL$**D,<,<  **,---sAA&A$A&cUR$r rar"s rr7WeightedRandomSampler.__len__rcr)rCr>r=rr)TN)r&r'r(r)r*rRTensorr9r:rErfloatrrr#r7r+r%rrrrss$\\ ! #%## #  #@.(3-.   rrcp\rSrSrSrS\\\\\4S\S\ SS4Sjr S\ \ \4S jr S\4S jrS rg) r ia=Wraps another sampler to yield a mini-batch of indices. Args: sampler (Sampler or Iterable): Base sampler. Can be any iterable object batch_size (int): Size of mini-batch. drop_last (bool): If ``True``, the sampler will drop the last batch if its size would be less than ``batch_size`` Example: >>> list(BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=False)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] >>> list(BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True)) [[0, 1, 2], [3, 4, 5], [6, 7, 8]] sampler batch_size drop_lastrNc[U[5(a[U[5(dUS::a[SU35e[U[5(d[SU35eXlX lX0lg)NrzBbatch_size should be a positive integer value, but got batch_size=z7drop_last should be a boolean value, but got drop_last=)rDr:rErGrrr)rrrrs rrBatchSampler.__init__-su:s++*d++QTU_T`a )T**I)U  $"rc#V# [UR5nUR(a&U/UR-n[ U6H n/UQv M g/[ R "XR5QnU(a0Uv /[ R "XR5QnU(aM/gg7fr )r2rrrzip itertoolsislice)r sampler_iterargsbatch_droplastbatchs rr#BatchSampler.__iter__FsDLL) >> >DOO3D"%t*'''#-Gi&&|__EFE J)**<IJ%s B#B)'B)cUR(a"[UR5UR-$[UR5UR-S- UR-$)Nrt)rr4rrr"s rr7BatchSampler.__len__TsI >>t||$7 7 %7!;O Or)rrr)r&r'r(r)r*r r r:rrErrlistr#r7r+r%rrr r si #ws|Xc]23## #  #2 K(49- KPPrr )rcollections.abcrrrrtypingrrr r rR__all__rr r:rr rrrr r%rrrs??44   4(4"gen4"d% %&H GCLH V!'#,!,> GCL> B?P749%?Pr